Optimization method of sub-connected active STAR-RIS auxiliary wireless communication system

Abstract

The invention discloses an optimization method of a sub-connection active STAR-RIS auxiliary wireless communication system. The optimization method comprises the following steps: S1, constructing a sub-connection active STAR-RIS auxiliary multi-user downlink communication system; s2, considering that K users are uniformly distributed in a STAR-RIS reflection area and a transmission area in a communication scene; s3, under the condition that the total emission energy of the base station and the total energy of the active STAR-RIS are limited, constructing a maximum system total energy efficiency optimization problem based on base station beam forming, an amplifier amplification coefficient, an STAR-RIS amplitude coefficient and a phase shift coefficient as optimization parameters; s4, giving base station beam forming and amplification coefficients and STAR-RIS amplitude coefficients and phase shift coefficients, and updating auxiliary variables; s5, an auxiliary variable, an amplification coefficient, an STAR-RIS amplitude coefficient and a phase shift coefficient are given, and base station beam forming is optimized; and S6, giving an auxiliary variable and optimal base station beam forming, and optimizing an amplifier amplification coefficient, an STAR-RIS amplitude coefficient and a phase shift coefficient. The hardware cost and the power loss of the system can be remarkably reduced, and the total energy efficiency of the system is improved.

Description

Technical Field

[0001] The invention relates to a transmission optimization method for a sub-connection active STAR-RIS auxiliary communication system, belonging to the technical field of wireless communication. Background Art

[0002] With the accelerated deployment of 5G commercial use, research on 6G is also gradually underway. Compared with 5G, 6G networks will accelerate the digitalization of the economy and society. In order to meet the business needs of larger bandwidth, higher speed, lower latency, and more intelligent services, higher working frequency bands, larger antenna scales, and stronger equipment computing power are important trends in the development of the next generation of mobile communication networks. However, the introduction of these new technologies will inevitably lead to more severe challenges in the development of mobile communication networks. The premise for the practical application of ultra-large-scale antenna technology is low cost, low power consumption, high reliability, and easy deployment. The large-scale access of 6G network equipment in the future will inevitably lead to a sharp increase in power consumption. How to achieve high-speed and low-power data transmission will become the key to the development of future networks. In order to solve this problem, Simultaneously Transmitting and Reflecting (STAR) Reconfigurable Intelligent Surfaces (STAR-RIS) came into being. As a two-dimensional implementation of electromagnetic metamaterials, STAR-RIS actively and intelligently regulates spatial electromagnetic waves in a programmable manner to form an electromagnetic environment with controllable amplitude, phase, polarization, and frequency, and intelligently reconfigures the wireless propagation environment, thereby significantly improving the performance of wireless communication networks. Because it can be deployed by using metamaterials and splicing, it has the advantages of low cost, low power consumption, low complexity and easy deployment. In addition, compared with reconfigurable smart surface (RIS), STAR-RIS can transmit and reflect the incident signal at the same time, expanding the service range from the half space of traditional RIS to the whole space, providing higher flexibility for signal propagation, and is considered to be one of the key technologies of 6G communication. In practical applications, due to the multiplicative fading effect introduced by passive STAR-RIS, the large path loss of the reflection link is caused. Many studies adopt an active STAR-RIS architecture to amplify the incident signal and strengthen the reflected signal through an external amplifier, thereby compensating for the large path loss of the reflection link and achieving higher capacity performance gain, that is, fewer units can achieve the same performance as traditional STAR-RIS, which means that the overhead of channel estimation and the complexity of the control link can be greatly reduced. Considering that different units of active STAR-RIS not only have different power shunt circuits and phase shift circuits to control the amplitude and phase shift coefficients, but also need to integrate a different amplifier outside each unit (hereinafter referred to as a fully connected structure) to power amplify the signal. When the number of STAR-RIS units is large, the fully connected structure consumes a lot of power and leads to higher hardware cost because a large number of power amplifiers are required. Summary of the invention

[0003] The present invention aims to address the defects and shortcomings of the above-mentioned prior art and proposes an optimization method for a wireless communication system assisted by a sub-connected active STAR-RIS. The method uses a sub-connected active STAR-RIS structure, divides all STAR-RIS units into several sub-arrays, and each sub-array unit shares an amplifier to amplify the signal, that is, multiple units in the sub-array are composed of different power shunt circuits and phase shift circuits and the same power amplifier (sub-connected structure). For the sake of comparison, it is assumed that STAR-RIS has a total of M units, and the M units are divided into L sub-arrays, and Q (Q=M / L) units of each sub-array share the same power amplifier. The fully connected structure can be regarded as a special sub-connected structure (when Q=1). When Q≠1, the number of power amplifiers in the sub-connected structure is only 1 / Q of the fully connected structure. Assuming M=1000 and Q=10, the power loss required for 1000 power amplifiers in the fully connected structure is 10W, while the sub-connected structure only requires 100 power amplifiers, and the power loss is reduced to only 1W. Therefore, this structure not only ensures full-space service for users, but also greatly reduces power loss and hardware costs on the basis of overcoming multiplicative fading and improving user service quality.

[0004] The present invention introduces sub-connected active STAR-RIS into the wireless communication system, and maximizes the energy efficiency of the system by jointly optimizing the beamforming of the base station, the amplification factor of the amplifier, and the amplitude and phase shift coefficient of the active STAR-RIS.

[0005] The technical solution adopted by the present invention to solve the technical problem is a method for optimizing a sub-connected active STAR-RIS assisted wireless communication system, the method comprising the following steps:

[0006] Step S1: construct a sub-connected active STAR-RIS assisted multi-user downlink communication system, which includes a multi-antenna base station, a sub-connected active STAR-RIS and K single-antenna users, wherein the STAR-RIS has a total of M units.

[0007] Step S2: The communication scenario is considered to be K users evenly distributed in the STAR-RIS reflection area and the transmission area. The base station transmits information to the users in the system through a direct link (base station to user) and a sub-connected active STAR-RIS reflection or transmission link.

[0008] Step S3: Under the condition that the total transmission energy of the base station and the total energy of the active STAR-RIS are limited, an optimization purpose of maximizing the total energy efficiency of the system is constructed based on the base station beamforming, the amplifier amplification factor, and the STAR-RIS amplitude coefficient and phase shift coefficient as optimization parameters, and the beamforming of the base station, the amplification factor of the amplifier, and the amplitude coefficient and phase shift coefficient of the active STAR-IRS are decoupled and optimized based on the alternating optimization algorithm.

[0009] Step S4: Given the base station beamforming, amplification factor, STAR-RIS amplitude coefficient and phase shift coefficient, update the auxiliary variables.

[0010] Step S5: Given auxiliary variables, amplification factors, STAR-RIS amplitude coefficients and phase shift coefficients, optimize the base station beamforming: Use the Dinkelbach algorithm, fractional programming and matrix transformation method to transform the original problem into a quadratic constrained quadratic programming problem about base station beamforming, and use the CVX optimization toolbox in MATLAB to solve the optimal beamforming.

[0011] Step S6: Given the auxiliary variables and the optimal base station beamforming, optimize the amplifier gain factor, STAR-RIS amplitude coefficient and phase shift coefficient: Use the Dinkelbach algorithm, fractional programming and matrix transformation method to transform the original problem into a quadratic constrained quadratic programming problem about the gain factor and STAR-RIS amplitude coefficient and phase shift coefficient, and use the CVX optimization toolbox in Matlab to solve the optimal solution. Continuously alternate the optimization and solution until convergence.

[0012] In step S1, the following contents are included:

[0013] The received signal of the kth user is:

[0014]

[0015] Where i∈{t,r}, f i,k represents the channel coefficient from the base station to user k, G represents the channel coefficient from the base station to the sub-connection active STAR-RIS, and h i,k represents the channel coefficient of the sub-connection active STAR-RIS to user k. in, represent the STAR-RIS amplitude matrix and phase shift matrix respectively, and denote the amplitude coefficient and phase shift coefficient of the mth unit of STAR-RIS, a=[a1,...,a L ] T is the magnification factor vector, a l represents the amplification factor of the lth amplifier, is the connection matrix. The signal sent by the base station is expressed as where w k and k are the beamforming vector of the base station and the desired signal of user k, respectively. z and n k They represent the dynamic noise at the active STAR-RIS and the additive white Gaussian noise at the user, respectively.

[0016] The SINR of user k is:

[0017]

[0018] H i,k H =f i,k H +h i,k H V i G.

[0019] According to Shannon's formula, the system and rate are:

[0020]

[0021] The total power consumption is:

[0022]

[0023] Where ξ and ζ represent the energy conversion coefficients at the BS and active STAR-RIS, respectively, and W U and W BS Represent the power dissipated by each user and base station, W PS and W PA Represents the power consumption of active STAR-RIS hardware (phase shifter and amplifier).

[0024] In step S2, in order to improve the overall energy efficiency of the system, an optimization problem is proposed:

[0025]

[0026] in, and They are the transmit power threshold of the base station and the power threshold of the active STAR-RIS. C1 represents the transmit power constraint of the base station; C2 represents the power constraint of the active STAR-RIS; C3 and C4 represent the phase shift constraint and amplitude constraint of the STAR-RIS respectively; and C5 represents the amplifier gain constraint.

[0027] In step S3, the Dinkelbach algorithm is first used to introduce the auxiliary variable η, and the objective function in problem (5) is transformed into an equation, and the optimal η opt satisfy

[0028]

[0029] The optimal η is solved by optimizing (7) opt

[0030]

[0031] stC1,C2,C3,C4,C5 (7)

[0032] The fractional programming method is used for the logarithmic function in R, and the auxiliary variable γ=[γ1,...,γ K ] T , transforming problem (7) into problem (8)

[0033]

[0034] stC1,C2,C3,C4,C5 (8)

[0035] in

[0036]

[0037] Introduce auxiliary variables τ=[τ1,...,τ K ] T , perform a secondary transformation on the fractional part of the objective function in problem (8) and convert it to problem (9)

[0038]

[0039] stC1,C2,C3,C4,C5 (9)

[0040]

[0041] Then, an alternating optimization algorithm is used to decouple the auxiliary variables, the base station's beamforming and the active STAR-RIS amplitude coefficient, phase shift coefficient and amplification factor, which is divided into three sub-problems: 1) given the base station beamforming, amplification factor and STAR-RIS amplitude coefficient and phase shift coefficient, update the optimal auxiliary variables; 2) given the optimal auxiliary variables, amplification factor and STAR-RIS amplitude coefficient and phase shift coefficient, optimize the base station beamforming; 3) given the optimal auxiliary variables and the optimal base station beamforming, optimize the amplification factor and STAR-RIS amplitude coefficient and phase shift coefficient.

[0042] In step S4, based on problem (9) in step S3, given the base station beamforming, amplification factor and STAR-RIS amplitude coefficient and phase shift coefficient, the auxiliary variables are updated:

[0043] Order respectively:

[0044]

[0045] The optimal γ k opt , optimal τ k opt .

[0046] In step S5, based on problem (9) in step S3, the base station beamforming is optimized by given the optimal auxiliary variables, amplification factors, and STAR-RIS amplitude coefficients and phase shift coefficients:

[0047] Remove the terms irrelevant to base station beamforming and transform problem (9) into

[0048]

[0049] Since the objective function in problem (10) is non-convex and the optimization variables are coupled with each other, in order to solve this problem, matrix transformation can be used for decoupling processing, and the optimization problem can be transformed into a quadratically constrained quadratic programming (QCQP) problem about base station beamforming. The optimization tool CVX is used to solve the optimal base station beamforming.

[0050] In step S6, based on problem (9) in step S3, given the optimal auxiliary variables and the optimal base station beamforming, the amplification factor and the STAR-RIS amplitude coefficient and phase shift coefficient are optimized:

[0051] By removing the terms that are not related to the amplification factor and the STAR-RIS amplitude coefficient and phase shift coefficient, problem (9) is transformed into:

[0052]

[0053] Since the objective function in problem (11) is non-convex and the optimization variables are coupled to each other, in order to solve this problem, matrix transformation can be used for decoupling, and the optimization problem can be transformed into a quadratic constrained quadratic programming (QCQP) problem about the amplification factor and the STAR-RIS amplitude coefficient and phase shift coefficient, and the optimization tool CVX is used to solve the optimal solution. Steps S4, S5, and S6 are iterated alternately until the total energy efficiency of the system converges.

[0054] Beneficial effects:

[0055] 1. Compared with the fully connected structure, the sub-connected active STAR-RIS structure of the present invention greatly reduces the number of power amplifiers in the hardware structure and significantly reduces the hardware cost.

[0056] 2. Compared with the fully connected structure, the sub-connected active STAR-RIS assisted wireless communication system model of the present invention not only ensures full-space service for users, but also greatly reduces power loss on the basis of overcoming multiplicative fading and improving user service quality.

[0057] 3. The optimization method of the sub-connected active STAR-RIS assisted wireless system transmission of the present invention achieves a higher system total energy efficiency through algorithm optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is a system model diagram of the present invention.

[0059] Figure 2 It is the algorithm flow chart of the present invention.

[0060] Figure 3 It is a comparison diagram of iterative convergence between the sub-connection structure and the full-connection structure of the present invention.

[0061] Figure 4 This is a comparison diagram of the relationship between the system efficiency and user location of the sub-connection structure and the full connection structure of the present invention.

[0062] Figure 5 This is a comparison diagram of the relationship between the total system energy efficiency and user location of the sub-connection structure and the full connection structure of the present invention. DETAILED DESCRIPTION

[0063] The invention will be further described in detail below in conjunction with the accompanying drawings.

[0064] like Figure 1 As shown, the present invention includes a sub-connected active STAR-RIS auxiliary communication system, which includes: a multi-antenna base station, K single-antenna users, and an active STAR-RIS, wherein the base station has N antennas, and the K single-antenna users are evenly distributed in the STAR-RIS reflection area and the transmission area. The active STAR-RIS contains M units, and the M units are evenly divided into L sub-arrays, and each sub-array Q elements share the same amplifier. A downlink transmission method is adopted. The base station transmits information to the users in the system through a direct link (base station to user) and a STAR-RIS reflection or transmission link. Assume that the channel coefficients from the base station to the user, the base station to the STAR-RIS, and the STAR-RIS to the user are: f i,k , G and h i,k , where i∈(r,t). All channels obey the small-scale Rician fading model. The amplitude matrix and phase shift matrix of STAR-RIS are and in, and denote the reflection or transmission amplitude coefficient and phase shift coefficient of the mth unit of STAR-RIS respectively. The amplification factor vector of the amplifier is a=[a1,...,a L ] T , a l represents the amplification factor of the lth amplifier. The received signal of the kth user is

[0065]

[0066] in, is the connection matrix. The signal sent by the base station is expressed as where w k and k are the beamforming vector of the base station and the desired signal of user k, respectively. z and n k They represent the dynamic noise at the active STAR-RIS and the additive white Gaussian noise at the user, respectively.

[0067] The SINR of user k is:

[0068]

[0069] H i,k H =f i,k H +h i,k H V i G.

[0070] According to Shannon's formula, the system and rate are:

[0071]

[0072] The total power consumption is:

[0073]

[0074] Where ξ and ζ represent the energy conversion coefficients at the BS and active STAR-RIS, respectively, and W U and W BS Represent the power dissipated by each user and base station, W PS and W PA Represents the power consumption of active STAR-RIS hardware (phase shifter and amplifier).

[0075] In order to improve the overall energy efficiency of the system, the optimization problem is proposed as follows:

[0076]

[0077] in, and They are the transmit power threshold of the base station and the power threshold of the active STAR-RIS. C1 represents the maximum transmit power constraint of the base station; C2 represents the power constraint of the active STAR-RIS; C3 and C4 represent the phase shift and amplitude constraints of the STAR-RIS respectively; and C5 represents the amplifier gain constraint.

[0078] Since η is a non-convex expression and the optimization variables are coupled, it is difficult to directly use the existing algorithm to solve the global optimal solution of the optimization problem. Therefore, an alternating iterative algorithm is used to optimize the problem. The specific ideas are as follows: 1) Fix the auxiliary variable τ and the base station beamforming vector w k , amplification factor vector a and STAR-RIS amplitude matrix E i With the phase shift matrix Θ i , update the auxiliary variable γ; 2) fix the auxiliary variable γ and the base station beamforming w k , amplification factor vector a and STAR-RIS amplitude matrix E i With the phase shift matrix Θ i , update the auxiliary variable τ; 3) fix the auxiliary variables γ, τ, the amplification factor vector a and the STAR-RIS amplitude matrix E i With the phase shift matrix Θ i , optimize the base station beamforming vector w k ; 4) Fix auxiliary variables γ, τ and the beamforming vector w of the base station k , optimize the amplifier gain vector a, STAR-RIS amplitude matrix E i With the phase shift matrix Θ i . Continue to iterate until the total energy efficiency of the system converges.

[0079] First, the objective function is processed. Since η is non-convex, the Dinkelbach algorithm is used to transform the objective function in problem (12) into an equation, and the optimal η opt satisfy

[0080]

[0081] The optimal η is solved by alternating optimization (14) opt

[0082]

[0083] stC1,C2,C3,C4,C5(14)

[0084] Introduce auxiliary variables γ=[γ1,...,γ K ] T , using fractional programming for fraction R, we can convert problem (14) into problem (15)

[0085]

[0086] stC1,C2,C3,C4,C5(15)

[0087] For g(w k ,a,E i ,Θ i ,γ), introduce auxiliary variables τ=[τ1,...,τ K ] T , use the quadratic transformation to transform problem (15) into problem (16)

[0088]

[0089] stC1,C2,C3,C4,C5(16)

[0090]

[0091] 1) Fixed auxiliary variable τ, base station beamforming vector w k , amplifier gain vector a and STAR-RIS amplitude matrix E i With the phase shift matrix Θ i , update the auxiliary variable γ;

[0092] make Get the best γ k opt for

[0093]

[0094] ρ k =R{τ k * H i,k H w k}.

[0095] 2) Fixed auxiliary variable γ and base station beamforming vector w k , amplifier gain vector a and STAR-RIS amplitude matrix E i With the phase shift matrix Θ i , update the auxiliary variable τ;

[0096] make Get the optimal τ k opt for

[0097]

[0098] 3) Fixed auxiliary variables γ, τ, amplifier gain vector a and STAR-RIS amplitude matrix Ei With the phase shift matrix Θ i , optimize the base station's active beamforming vector w k ;

[0099] The original problem can be restated as

[0100]

[0101] make By matrix transformation and removing constant terms, problem (19) is transformed into problem (20)

[0102]

[0103]

[0104] This problem is a quadratically constrained quadratic programming (QCQP) problem, and the optimal W can be directly solved using the optimization tool CVX.

[0105] 4) Fixed auxiliary variables γ, τ, base station beamforming vector w k , optimize the amplifier gain vector a and the STAR-RIS amplitude matrix E i With the phase shift matrix Θ i .

[0106] The original problem can be restated as

[0107]

[0108] Let α i,k,j =f i,k H w j , β j =Gw j ,but

[0109]

[0110] in,

[0111] By matrix transformation and removing constant terms, problem (21) can be transformed into problem (22)

[0112]

[0113] This problem is a quadratically constrained quadratic programming (QCQP) problem, and CVX can be used to directly solve the optimal The above process is iterated repeatedly until the total energy efficiency of the system converges. The specific algorithm flow is as follows: Figure 2 .

[0114] The practical effects of the present invention are described in detail below in conjunction with simulation. Specifically, it includes:

[0115] 1) Simulation conditions

[0116] like Figure 1 As shown, the system of the present invention consists of a multi-antenna base station, 4 mobile single-antenna users (2 transmission users, 2 reflection users) and an active STAR-RIS. Assume that the base station has 6 antennas, STAR-RIS has 256 units, and all units are evenly divided into L subarrays, each subarray has Q units, and the path loss from the base station to the user, to the STAR-RIS, and from the STAR-RIS to the user is 2. Assume that the base station coordinates are (0, 0, 0), the STAR-RIS coordinates are (0, 50, 0), and the positions of the reflection user and the transmission user change dynamically, and are located in the reflection area and the transmission area of ​​the STAR-RIS respectively.

[0117] 2) Simulation results

[0118] In this example, three schemes are designed for comparison, namely a full connection structure and two different sub-connection structures (L=16 and L=1). Figure 3 The convergence of the algorithm designed by the present invention under different numbers of active STAR-RIS subarrays, Figure 4 and Figure 5 They are the changes of system rate and total energy efficiency when users are in different locations. Figure 3 It can be observed that all three schemes converge, and when the number of active STAR-RIS subarrays L = 1 and L = 16, the total energy efficiency of the communication system designed by the present invention is better than that of the fully connected structure. Figure 4 , Figure 5 It can be observed that: when the reflected user and the transmitted user gradually move away from the base station and the active STAR-RIS, the system sum rate and the total energy efficiency gradually decrease, and the system sum rate achieved by the fully connected structure is higher than the system sum rate achieved by the sub-connected structure (L=1, L=16), while the system total energy efficiency achieved by the fully connected structure is lower than the system total energy efficiency achieved by the sub-connected structure. In addition, the system sum rate achieved when L=1 is lower than the system sum rate achieved when L=16, but the total energy efficiency achieved when L=1 is higher than the system total energy efficiency achieved when L=16.

[0119] The above description is only a preferred implementation example of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An optimization method for a sub-connected active STAR-RIS assisted wireless communication system, characterized in that: The method comprises the following steps: Step S1: constructing a multi-user downlink communication system assisted by a sub-connected active STAR-RIS, the system comprising a multi-antenna base station, a sub-connected active STAR-RIS and K single-antenna users, wherein the STAR-RIS has a total of M units; Step S2: The communication scenario is considered to be that K users are evenly distributed in the STAR-RIS reflection area and the transmission area, and the base station transmits information to all users in the system through a direct link (base station to user) and a sub-connected active STAR-RIS reflection or transmission link; Step S3: Under the condition that the total transmission energy of the base station and the total energy of the active STAR-RIS are limited, an optimization purpose of maximizing the total energy efficiency of the system is established based on the base station beamforming, the amplifier amplification factor, and the STAR-RIS amplitude coefficient and phase shift coefficient as optimization parameters, and the beamforming of the base station, the amplification factor of the amplifier, and the amplitude coefficient and phase shift coefficient of the active STAR-IRS are decoupled and optimized based on the alternating optimization algorithm; Step S4: given the base station beamforming, amplification factor and STAR-RIS amplitude coefficient and phase shift coefficient, update the auxiliary variables; Step S5: Given auxiliary variables, amplification coefficients, and STAR-RIS amplitude coefficients and phase shift coefficients, optimize the base station beamforming: Use the Dinkelbach algorithm, fractional programming, and matrix transformation method to transform the original problem into a quadratic constrained quadratic programming problem about base station beamforming, and use the CVX optimization toolbox in Matlab to solve the optimal beamforming; Step S6: Given the auxiliary variables and the optimal base station beamforming, optimize the amplifier gain, STAR-RIS amplitude coefficient and phase shift coefficient: Use the Dinkelbach algorithm, fractional programming and matrix transformation method to transform the original problem into a quadratic constrained quadratic programming problem about the gain and STAR-RIS amplitude coefficient and phase shift coefficient. Use the CVX optimization toolbox in MATLAB to solve the optimal solution, and continuously iterate the optimization solution until convergence.

2. The optimization method of a sub-connection active STAR-RIS assisted wireless communication system according to claim 1, characterized in that: In step S1, Includes the following: The received signal of the kth user is: Where i∈{t,r}, f i,k represents the channel coefficient from the base station to user k, G represents the channel coefficient from the base station to the sub-connection active STAR-RIS, and h i,k represents the channel coefficient of the sub-connection active STAR-RIS to user k, in, represent the STAR-RIS amplitude matrix and phase shift matrix respectively, and denote the amplitude coefficient and phase shift coefficient of the mth unit of STAR-RIS, a=[a1,...,a L ] T is the magnification factor vector, a l represents the amplification factor of the lth amplifier, is the connection matrix, and the signal sent by the base station is expressed as where w k and k are the beamforming vector of the base station and the desired signal of user k, z and n respectively. k denote the dynamic noise at the active STAR-RIS and the additive white Gaussian noise at the user, respectively. The SINR of user k is: According to Shannon's formula, the system and rate are: The total power consumption is: Where ξ and ζ represent the energy conversion coefficients at the BS and active STAR-RIS, respectively, and W U and W BS Represent the power dissipated by each user and base station, W PS and W PA Represents the power consumption of active STAR-RIS hardware (phase shifter and amplifier).

3. The optimization method of a sub-connection active STAR-RIS assisted wireless communication system according to claim 1, characterized in that: In step S2, in order to improve the overall energy efficiency of the system, the optimization problem is proposed as follows: in, and They are the transmit power threshold of the base station and the power threshold of the active STAR-RIS, respectively. C1 represents the maximum transmit power constraint of the base station; C2 represents the power constraint of the active STAR-RIS; C3 and C4 represent the STAR-RIS phase shift constraint and amplitude constraint respectively; C5 represents the amplifier gain constraint.

4. The optimization method of a sub-connection active STAR-RIS assisted wireless communication system according to claim 1, characterized in that: In step S3, the Dinkelbach algorithm is first used to introduce the auxiliary variable η, and the objective function in problem (5) is transformed into an equation, and the optimal η opt satisfy: The optimal η is solved by optimizing (7) opt The fractional programming method is used for the logarithmic function in R, and the auxiliary variable γ=[γ1,...,γ K ] T , problem (7) is equivalently transformed into problem (8): in: Introduce auxiliary variables τ=[τ1,...,τ K ] T , perform a secondary transformation on the fractional part of the objective function in problem (8) and convert it to problem (9): Then, an alternating optimization algorithm is used to decouple the auxiliary variables, the base station's beamforming and the active STAR-RIS amplitude coefficient, phase shift coefficient and amplification factor, which is divided into three sub-problems: 1) given the base station beamforming, amplification factor and STAR-RIS amplitude coefficient and phase shift coefficient, update the optimal auxiliary variables; 2) given the optimal auxiliary variables, amplification factor and STAR-RIS amplitude coefficient and phase shift coefficient, optimize the base station beamforming; 3) given the optimal auxiliary variables and the optimal base station beamforming, optimize the amplification factor and STAR-RIS amplitude coefficient and phase shift coefficient.

5. The optimization method of a sub-connection active STAR-RIS assisted wireless communication system according to claim 1, characterized in that: In step S4, based on problem (9) in step S3, given the base station beamforming, amplification factor, STAR-RIS amplitude coefficient and phase shift coefficient, the auxiliary variables are updated: Order respectively: Get the best Optimal 6. The optimization method of a sub-connection active STAR-RIS assisted wireless communication system according to claim 1, characterized in that: In step S5, based on problem (9) in step S3, the optimal auxiliary variables, amplification factors, STAR-RIS amplitude coefficients and phase shift coefficients are given to optimize the base station beamforming: By removing the items irrelevant to the base station beamforming, problem (9) is transformed into: Since the objective function in problem (10) is non-convex and the optimization variables are coupled with each other, matrix transformation is used for decoupling and the optimization problem is transformed into a quadratically constrained quadratic programming (QCQP) problem for base station beamforming. The optimization tool CVX is used to solve the optimal base station beamforming.

7. The optimization method of a sub-connection active STAR-RIS assisted wireless communication system according to claim 1, characterized in that: In step S6, based on problem (9) in step S3, given the optimal auxiliary variables and the optimal base station beamforming, the amplification factor and the STAR-RIS amplitude coefficient and phase shift coefficient are optimized: Remove the terms that are independent of the amplification factor and the STAR-RIS amplitude coefficient and phase shift coefficient, and transform problem (9) into Since the objective function in problem (11) is non-convex and the optimization variables are coupled with each other, in order to solve this problem, matrix transformation can be used for decoupling processing, and the optimization problem can be transformed into a quadratic constrained quadratic programming (QCQP) problem about the amplification factor and the STAR-RIS amplitude coefficient and phase shift coefficient. The optimization tool CVX is used to solve the optimal solution, and the above steps S4, S5 and S6 are continuously iterated alternately until the total energy efficiency of the system converges.

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